Where's the Transparency in Order Routing?

Buy-side firms are diving below the surface to find out where their orders are being routed.

It's good to have your order filled in a timely manner. In fact, the equities markets depend on fast and accurate executions. But with the proliferation of trading venues and the growing complexity of the equity trading markets, buy-side traders are looking for more information about how their orders got from point A to point B.

As a result, buy-side institutions are asking for more granular data about where their orders are being routed and how many hops their orders took before they were ultimately executed. Transparency is a hot topic on the buy side as traders grapple with today's complex market structure, which has caused them to increasingly rely on algorithms and smart order routers to spread their orders across multiple venues.

Since algos slice and dice orders and route them to a host of dark pools and exchanges, it's not always clear as to how many hops the order has taken before it's executed. The many hops could potentially hurt execution, resulting in increased costs or lower margins on a trade.

"It's probably the biggest issue that we're spending time on now with clients," says Justin Schack, partner and managing director at Rosenblatt Securities, an institutional agency broker that specializes in market structure.

The trend is fueled by the structural complexity of U.S. equity trading, where orders can be routed and split among a combination of 13 exchanges and 40 dark pools. "Before the great transformation that began in the mid- to late '90s, we had a market structure that was pretty simple, easy to understand and highly manual," says Schack. "Over the past 16 years, we've moved to a place where the market structure has become infinitely complex, highly automated and where the ways for an order to be handled that are advantageous to the intermediary are greater than in the past."

Sophistication Breeds Confusion

As a sign of this complexity, certain exchanges have created esoteric order types that may help high-frequency trading firms at the expense of ordinary investors. The Securities and Exchange Commission is said to be investigating these order types.

With more sophisticated electronic tools on the desktop today, the buy side in theory has a lot more control over the selection of its orders and trading strategies. "However, once the parent order is placed, it goes into this Rube Goldberg market structure, and the child orders can get routed in ways that are counter to the client's best interests," Schack says.

The rise of dark pools -- private venues that allow institutions, mutual funds and hedge funds to match orders anonymously at prices that are hidden from view -- has created the need for more data analysis on the part of the buy side, industry participants contend.

The Pipeline Trading Systems' scandal may have amplified the concerns. In October 2011, the SEC fined Pipeline, a dark pool operator, for breaching the confidentiality of client orders to a proprietary trading affiliate. This past October, the SEC fined another broker, eBX, which operated the LeveL ATS dark pool, for the way it shared data with Lava Trading's smart order router. Both incidents alarmed the buy side and underscored the need for transparency into routing practices.

Today's buy-side trader has become more sophisticated and is asking many more questions. "They are light-years away from where they were two years ago," says Joe Saluzzi, partner and co-founder of Themis Trading, an institutional brokerage firm in Chatham, N.J. "They realize that they need to know more. Saluzzi, who is the co-author of Broken Markets (FT Press, 2012), says it's clients' fiduciary responsibility to ask about trade execution. "You need to know what's inside. You can't just slap the box and walk away," says Saluzzi, referring to computer-driven algorithms.

One concern is that brokers may have an economic incentive to execute orders through venues that give them a rebate, which helps the broker generate revenue. "If you are trading 100,000 shares through a VWAP algorithm, of course it's going to check the dark pools that are free and the inverted exchanges," says Saluzzi. Inverted exchanges pay the trader who is taking liquidity a higher rebate than someone who is adding liquidity. "The economics make sense for them," says Saluzzi, referring to brokers that tap the inverted exchanges for rebates. "It's up to the client to see if they're getting slippage, and if they are," they need to wake up, he suggests.

"Even if you ask the question once, you need to constantly stay on it," he adds. "And the brokers are responding. They realize they have to, because the clients are smarter."

Asset managers are also moving forward to do more forensic analysis of the data that's generated by their executions. The data is available from the sell side via Tag 30, a specific message standard used by the buy and sell sides to transmit their orders.

"Obviously, the driver is transparency," says Dave Hagen, VP of global trading technologies at Linedata. "If I'm trading with an algo, the question is, where am I actually getting filled?" says Hagen, a former floor trader who has worked at several sell-side firms and in wealth management.

Tag 30: A Data Deluge?

"It can be an overwhelming amount of data. It can be a challenge to the sell side," says Hagen. In fact, some sell-side electronic trading executives have questioned whether the buy side is going to do anything with the information, or whether it's just requesting it, says Hagen."The buy side is now asking for a lot more information that I think is going to be potentially challenging for the sell side," he says. The sell side is also asking how to best deliver the data to the buy side, notes Hagen, adding this was a hot topic at Linedata's user conference in November.

Going back two or three years, the sell side was not passing on Tag 30 data; you had to request it, and it was up to the broker whether to provide it, recalls Hagen. About three years ago, Linedata began to take Tag 30 data into its database, though it never displayed it to users of its order management system. Then one of its clients asked the software company to think about displaying that information. At the request of one large buy-side client, Linedata began showing the Tag 30 data in real time. Hagen says the client's feedback has been eye opening at times. "It helps them to make decisions in flight," he says, adding that this is "the cutting edge when it comes to data visualization of real-time trading data."

Linedata's TraderPlus tool is able to display the results of Tag 30 to the buy side in real time. "That is morphing in real time so you can see what's going on," Hagen says. "Trying to display all the hops ... in a meaningful way is going to be a challenge. There is too much data."

But the interest in analyzing smart order-routing data could grow. "Now that Tag 30 is kind of a given, they [buy-side clients] want to know where it went before it got filled in the high-speed markets," says Hagen. "That can be a lot of data in today's high speed trading market."

A number of buy-side firms take the execution destinations back via FIX. This allows them to capture fill-by-fill destination information, says Frank Troise, global head of equities electronic client Solutions at JPMorgan. Firms may take that data in real time or end of day, or at different periods of times, he adds.

Ivy is Editor-at-Large for Advanced Trading and Wall Street & Technology. Ivy is responsible for writing in-depth feature articles, daily blogs and news articles with a focus on automated trading in the capital markets. As an industry expert, Ivy has reported on a myriad ... View Full Bio

With high transaction rates from algorithmic trading and usage of smart order routers sending small orders to multiple venues, I agree the storage requirements could be onerous. This is where cloud-based storage can can help the buy side archive their transaction data. EMS and OMS vendors that are offering cloud-based or Software-as-a-Service (Saas) models can offer solutions.

Trade should benefit all parties to the transaction, and it should be seen as doing so. The more complex the process, the more important transparency becomes. Perhaps in the case of very high transaction volumes, some leeway could be given on storage requirements and some kind of analysis could be applied to verify the propriety of transactions soon after the trade.